1. Field of the Invention
The present invention generally relates to image processing, and specifically, a method and an apparatus for expressing a motion object in the computer vision technology.
2. Description of the Related Art
The image processing technology related to a motion object has various aspects, such as the recognition of an object itself, the recognition of the appearance of a motion object, the prediction of the movement of a motion object in a case of a moving person. Other examples include gesture recognition, object gender recognition, object age recognition, object emotion recognition or the like. These recognitions have important usage, for example, a human-computer interaction can be performed based on gesture recognition, an advertisement push can be performed based on object gender recognition, population statistics can be performed based on gender recognition and age recognition, and transformation of a human-computer interaction interface can be performed based on emotion recognition.
Generally, the image processing technology for automatically analyzing a motion object relates to three phases, feature extraction, model modeling or classifier training, and test sample analysis using the model or classifier. The feature extraction is very important.
During human motion such as a walking process, it is difficult to perform recognition for human-motions, because the viewing angles of a camera are different and images are different.
Technology for recognizing human-actions based on images have been provided.
The U.S. Pat. No. 8,189,866B1 discloses the technology of human-action recognition. In such technology, the human-actions in an image are recognized by providing extracted feature vectors to a classifier using a low-level feature (skin color, body edge or the like) detector and a high-level feature (a human face or the like) detector.
The U.S. Pat. No. 8,345,984B2 discloses the technology of human action recognition. In such technology, motion information in multiple adjacent frames is captured by performing 3D convolutions, and features are extracted from spatial and temporal dimensions; multiple channels of information is generated from video frames, the multiple channels of information is combined to obtain a feature representation for a 3D CNN model, and the 3D CNN model is applied to recognize human actions.
The U.S. Pat. No. 7,330,566B2 discloses the technology of motion object recognition based on a gait. In such technology, a subject is identified based on extracted stride length, cadence and height parameters of the subject.
The U.S. Pat. No. 7,212,651B2 discloses the technology of detecting a moving object in a temporal sequence of images. In such technology, images are selected from the temporally ordered sequence of images, a set of functions is applied to the selected images to generate a set of combined images, a linear combination of filters is applied to a detection window in the set of combined images to determine motion and appearance features of the detection window, and the motion and appearance features are summed to determine a cumulative score, which enables a classification of the detection window as including the moving object.